• 대한공업교육학회지
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• 제30권1호
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• pp.19-36
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• 2005

The purpose of this study is to develop the Public Practice Center's teaching-learning model by applying blended learning strategies which is complementary to the expected problems such as expansion of the educational object and diversity of the curriculum to maximize the educational effect and to analyze activation types of the Practical Practice Center to expand the Public Practice Center's function and role by studying the document. Blended Learning Strategies are established in consideration of the following eight (8) factors ; learning environment, learning purpose, learning contents, learning time, learning place, learning type, learning media, type of interaction. It is redesigned and amended to the KEDI's individual confirmation instruction model for skill learning (1975) which is considered to be effective in the filed of education by applying features, educational contents of the Public Practice Center's teaching and merit of Blended Learning Strategies simultaneous. This model is composed of six (6) steps as shown below; 1. Understanding on the purpose and orientation 2. Observation for demonstration of fundamental skill 3. Ex on-line learning 4. Acquirement of element skill 5. Confirmation for acquirement of fundamental skill 6. After on-line learning. Further to this, this model is designed so that the above eight factors will be applied to the students effectively and the merit of e-learning and off-line practice will be mixed to the learner's expectation and satisfaction.

• 한국환경성돌연변이발암원학회:학술대회논문집
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• 한국환경성돌연변이발암원학회 2004년도 KSOT/KEMS Fall Annual Meeting
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• pp.153-153
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• 2004

• 산업융합연구
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• 제17권3호
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• pp.1-8
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• 2019

본 연구는 스포츠센터 지도자들의 일터 학습(work place learning)과 관련하여 구성주의 관점 및 무형식 학습의 특성에 대해 이론적인 접근을 통한 고찰을 하였다. 이에 무형식 학습을 기반으로 하는 스포츠센터 지도자는 그 성장과정과 학습을 촉진 시키는 과정에서 경험과 실천적 측면에서의 일터 학습을 통한 전문성 신장이 될 수 있도록 하기 때문에 무형식 학습은 중요한 학습적 의미를 갖는다고 할 수 있다. 또한, 스포츠센터 지도 현장에서의 지도자는 일반적인 기업의 사무직 근로자에 비해 상대적으로 일터 학습에서 형식적인 학습의 기회가 부족하다고 할 수 있다. 따라서 무형식 학습의 유형 및 학습 향상에 대한 방안의 제시가 이루어져야 할 것이며 이러한 부분은 스포츠센터 지도자의 전문성 신장을 위한 중요한 요소로서 교육적 의미가 있다고 판단된다. 아울러 개인적, 환경적, 제도적, 조직적 측면에서 직장 내 학습 환경의 구축이 이루어진다면 스포츠센터 지도자들의 전문성 신장에 큰 도움이 될 것이라 생각된다.

• Korean Journal of Radiology
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• 제22권12호
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• pp.2073-2081
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• 2021

Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases and innovation. Radiologists can act as important mediators between deep learning and medicine by simultaneously playing pioneering and gatekeeping roles. The application of deep learning technology in medicine is sometimes restricted by ethical or legal issues, including patient privacy and confidentiality, data ownership, and limitations in patient agreement. In this paper, we present an open platform, MI2RLNet, for sharing source code and various pre-trained weights for models to use in downstream tasks, including education, application, and transfer learning, to encourage deep learning research in radiology. In addition, we describe how to use this open platform in the GitHub environment. Our source code and models may contribute to further deep learning research in radiology, which may facilitate applications in medicine and healthcare, especially in medical imaging, in the near future. All code is available at https://github.com/mi2rl/MI2RLNet.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2012년도 춘계 종합학술대회 논문집
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• pp.207-208
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• 2012

본 연구의 목적은 e-포트폴리오인 K-folio의 개발 배경, 목적과 전략 그리고 애플리케이션 구성 등을 소개하고 추후 발전 방향을 제시하는 것이다.

• 한국정보컨버전스학회논문지
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• 제7권1호
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• pp.17-24
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• 2014

본 연구는 세계적 수준의 전문대학(WCC) 교수학습센터의 웹서비스 현황을 분석하였다. WCC란 산업체의 요구를 수용할 수 있는 교육여건을 갖추고 성장가능성과 비전을 갖춘 전문대학을 말한다. WCC의 교수학습센터에서 지원하는 웹서비스는 센터소개, 교수지원, 학습지원, 서비스, 특화된 메뉴로 구성되어 있다. 총 21개 대학 중 웹서비스를 제공하고 있는 9대 대학의 메뉴와 대학별 특화된 웹서비스를 분석하였다. 세계적 수준의 전문대학 교수학습센터의 웹서비스를 분석함으로써 전문대학 교수학습센터가 지향하는 교수학습센터 웹서비스의 방향을 제시하고자 한다.

• 한국보육지원학회지
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• 제15권6호
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• pp.189-206
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• 2019

Objective: The objective of this study was to develop and apply a Teacher Learning Community Program in order to improve the specialization of infant daycare center teachers and explore the changes in the learning attitudes of teachers. Methods: To develop the program, the requirements were analyzed by surveying 500 teachers of infant classes. The developed program was provided to 25 infant daycare center teachers for a total of 14 sessions. A total of 75 sets of collected journal writing materials were analyzed qualitatively. Results: First, the Teacher Learning Community Program for infant daycare center teachers was developed. Second, the Teacher Learning Community Program was found to bring a shift in the learning attitudes among the teachers of infant classes towards reflective and communal learning. Conclusion/Implications: The Teacher Learning Community Program brought a shift in the learning attitude towards reflective and communal learning. Thus, the Teacher Learning Community Program can be applied as a teacher education program for improvement of the specialization of infant daycare center teachers.

• 한국HCI학회:학술대회논문집
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• 한국HCI학회 2006년도 학술대회 1부
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• pp.519-525
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• 2006

Advances in information and telecommunication technology increasingly reveal the potential of computer supported education. However, most computer supported learning systems until recently did not pay much attention to different characteristics of individual learners. Intelligent learning environments adaptive to learner's preferences and tasks are desired. Each learner has different preferences and needs, so it is very crucial to provide the different styles of learners with different learning environments that are more preferred and more efficient to them. This paper reports a study of the intelligent learning environment where the learner's preferences are diagnosed using learner models, and then user interfaces are customized in an adaptive manner to accommodate the preferences. In this research, the learning user interfaces were designed based on a learning-style model by Felder & Silverman, so that different learner preferences are revealed through user interactions with the system. Then, a learning style modeling is done from learner behavior patterns using Decision Tree and Neural Network approaches. In this way, an intelligent learning system adaptive to learning styles can be built. Further research efforts are being made to accommodate various other kinds of learner characteristics such as emotion and motivation as well as learning mastery in providing adaptive learning support.

• 한국정보시스템학회:학술대회논문집
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• 한국정보시스템학회 2005년도 춘계학술대회 발표 논문집
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• pp.409-424
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• 2005

Today, many universities are confronted with the changing education paradigm such as e-Learning, Distance Education, Virtual University. This IT-based learning paradigm shift is certainly a new opportunity or a threat to our universities. The Local University Community e-Learning Centers that support the demand of e-Learning for their community are recommended. Tn order to operate these centers efficiently, the strategic roles of the e-Learning center should first be defined. To define the strategic roles, We classified the strategic roles of the e-Learning center into four dimensions, (1) to improve management efficiency, (2) to enhance educational service, (3) to acquire competitive advantages, (4) to build new education infrastructure, and each dimension has S or 6 measurement items. As result, to enhance the educational service was considered as the most significant factor among the four dimensions of strategic roles, and the infrastructure building was the next. Through the strategic roles definition and analysis of expected role ratings, we could have recommended the direction and operation policies of the e-Learning centers.

• Korean Journal of Radiology
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• 제24권11호
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• pp.1061-1080
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• 2023

Artificial intelligence (AI) in radiology is a rapidly developing field with several prospective clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of Radiology held a forum to discuss the challenges and drawbacks in AI development and implementation. Various barriers hinder the successful application and widespread adoption of AI in radiology, such as limited annotated data, data privacy and security, data heterogeneity, imbalanced data, model interpretability, overfitting, and integration with clinical workflows. In this review, some of the various possible solutions to these challenges are presented and discussed; these include training with longitudinal and multimodal datasets, dense training with multitask learning and multimodal learning, self-supervised contrastive learning, various image modifications and syntheses using generative models, explainable AI, causal learning, federated learning with large data models, and digital twins.